CREACIÓN DE APTITUDES CENTRALES

Data Collection Instruments

Primary data will be collected through questionnaire based on a 5-point Likert scale with mixed closed and open-ended questions targeting corporate management of the stakeholders in the maritime cluster groups as shown in table3.3. These are the preferred methods since they are easy to administer and time-saving (Mugenda & Mugenda, 2008). Each item on the questionnaires are developed on specific objectives of the study, which will be self- administered to the respondents in accordance with (Orodho, 2008). The study will also make use of key informant interviews from the maritime cluster firms’ management of service providers table 3.3. Further, secondary data for this study will be collected through reviews of maritime studies of professional journals, textbooks and e-books for analysis purpose (appendix iii). The advantage of the secondary data is to compare findings from the field with the existing body of knowledge. The combination of the findings are for better understanding of the problem and to enhance existing body of knowledge as reviewed from the different sources to either corroborate or disagree with the findings (Creswell, 2013).

Data Collection Procedure

Data collection is defined as precise, systematic gathering of information relevant to research problem by use of methods such as interviews, participant observation, narrative or case histories (Burns & Grove, 2003). The management of the maritime cluster firms will be the target group and will be contacted for appointment or permission to allow data collection. An introductory letter from the university (JKUAT) and permit from National Commission for

Science, Technology and Innovation (NACOSTI) will be obtained before proceeding to the field for data collection. The study will seek the services of eight (8) research assistants who will be trained on the use of study research tools and will be regularly supervised to insure consistency and assistance that may be required during the entire period of data collection. The study envisions collecting the data for this study during the months of April to May, 2018 for the period of 30 days.

Both quantitative and qualitative data collections will be used for the study from the respondents and the management team in the maritime cluster firms is the target group. The data collection will be through questionnaires and by face-to-face interviews. The questionnaires will be self-administered by drop, and pick method, each respondent will receive verbal instructions asking them to complete the questionnaires anonymously, whereas document analysis guide be used for gathering the secondary data and enough time will be granted to respond to all the questionnaires.

Pilot Testing

Pilot testing will be undertaken to support reliability and validity (Kothari, 2004). Pilot testing is to ascertain the reliability and validity of the research instruments before being administered to the target population. The study will be conducted with 10% of the total sample size of 94 maritime cluster firms that will not be included in the final research. This is in accordance with recommendations by (Bryman & Bell, 2015). Pilot testing will provide an opportunity to detect and remedy any potential problems with the research instruments (questionnaires).

Validity of Research Instruments

According to Field (2009), determining the accuracy of the scale measurements in a research instrument is of great importance in scientific studies. The study will employ both construct and content validities and will use confirmatory factor analysis to test for validity.

For construct validity, the study will align the research instruments with the research objectives by use of factor analysis whereas in conducting content validity, the study will seek the assistance of the supervisors. Saunders et al (2009) define content validity as the extent to which the measurement tool provides adequate coverage of the investigative questions. Construct validity therefore refers to the extent to which the measurement questions actually measure the presence of the constructs intended to be measured (Saunders et al., 2009). Whereas according to Cooper & Schindler (2014), construct validity entails looking at both the theory and the measuring instrument being used.

Reliability of Research Instruments

The reliability test is conducted to measure whether the research instrument will provide the same results each time the instrument is used among the sample respondents. Reliability problems may arise when studies tend to overstate the importance of the data obtained or the sample is too restricted (Sullivan, 2011). The common method for testing the reliability of a research instrument is by using the Cronbach Alpha’s reliability coefficient, α, will be used for internal reliability test. The coefficient normally ranges between 0 and 1 although actually no lower exists. The closer α, is to 1.0 the greater the internal consistency of the items in the scale. The size of α will be determined by both the number of items in the scale and the mean inter- item correlations based upon the formula:

α= !" #$%&'₍

Where: K= is the number of items considered and r = is the mean of inter-item correlations. George and Malley (2003) as cited in Wanjala, Iravo, Odhiambo and Shalle (2017) provide the following commonly accepted rules of thumb for alpha values:

α ≥ 0.9 – Excellent ; 0.9 .>α≥ 0.8 – Good; 0.8 ≥ α≥ 0.7 – Acceptable; 0.7 >α≥ 0.6 - Questionable; 0.6.>α≥ 0.5 – Poor and 0.5 >α– unacceptable. Therefore, ideally the Cronbach Alpha Coefficient of the scale should be at least acceptable, that is, above 0.7 (Bryan, 2014).

Data Analysis

The study will employ both descriptive and inferential statistics and will further undertake diagnostics tests to ensure accuracy and fitness of the data by testing of the hypothesis developed (Sekaran, 2006). The quantitative data analysis will involves a series of statistical activities generated by Statistical Package for Social Science SPSS such as mean, percentages, variances, figures, charts, T-test and infographics to give expected summary of variables being studied for ease of interpretation and presentation whereas qualitative data will be presented through narratives in line with the research objectives.

The collected qualitative data will be analysed using content analysis in line with the study objectives and will be presented in form of frequency, mean, measurement of relationship and standard deviation. The resultant thematic contents analysis and patterns will be assigned numbers for measurement purposes.

Both inferential and descriptive statistics methods will be used to analyse data obtained from the questionnaire using SPSS (version 22.0) to give expected summary of variables of the study. The study will triangulate both qualitative and quantitative data from the respondents who will answer the questionnaire. To test the significance of the relationship between variables, the study will use both correlation and regression analyses. Inferential Statistics will measure the relationship between the independent variables and dependent variable by use of a multiple regression. Mugenda and Mugenda (2008) assert that multiple regression analysis attempts to determine whether a group of variables together predicts a given dependent variable and in this way, attempt to increase the accuracy of the estimate.

Descriptive Statistics analysis

In this case, descriptive statistics will be used to give information that describes the data. The results will be represented through the use of graphs, charts and tables such as histograms, bar graphs and pie graphs. This method will ensure that the data is described by compiling it into a graph, table or other visual representation. This will provide an easier method for making comparisons between different data sets and to spot the smallest and largest values and trends or changes over a period of time.

Inferential Statistics Analysis

The research will use regression analysis to assess the relationship between the independent variables and dependent variable using SPSS version 24.0 and regression analysis to be used will be:

Y = α + β1X1 + β2X2 + β3X3+ β4X4 +β5 X5 + ε

Where: Y = Dependent Variable (organizational performance) Independent variables, which include:

X1 is strategic policies

X3 is Resource Capability

X4 is System and Processes

X5 is strategic direction

α = the constant = Y intercept

β1- 5 = the regression coefficient or change included in Y by each X

є = error term

The closer the p-values of the regression results are closer to +1 the higher the association between the research variables.

Moderating Variable Analysis

The moderated multiple regression model will be;

Y = α +bX + cZ + dX*Z+ ε Where;

Y= Organization performance

X= Aggregate influence of strategy implementation

Z= Hypothesized moderation of size and age of organization on relationship between X on Y dX*Z = The composite influence of strategy implementation and firm characteristics on organization performance.

ε = Error term

Data processing

The t-test will be used to determine the relationship between each individual independent variable and the dependent variable. The F- test will be used to determine the overall relationship between the dependent and all the independent variables.

Statistical Tests

In this study, the hypothesis testing will be carried out by using both t-test and F-test statistical tools with 0.05significance level. Once the model is analysed, value of 0.05 and obtained for each hypothesis and a P-value then the hypothesis will be rejected. The purpose of hypothesis testing is to determine the accuracy of the study hypotheses because the researcher has collected a sample of data, not a census (Cooper & Schindler, 2008).

Hypothesis Testing

The study will adopt statistical tests such as ANOVA (Goodness of Fit Tests) and independent t- tests at a 95% Confidence level. The statistical tests will enable the study to accept or reject the research hypotheses based on the statistical significance of the results. The closer the statistical values will be to 1 the higher the statistical association between the variables.

Diagnostic Tests

This section contains diagnostics tests for testing regression assumptions, which include; Linearity, Normality, Heteroscedasticity, Multicollinearity and Tests of Independence (Autocorrelation) as shown in table 3.4.

Table 3.4 Diagnostics test

Test Significance Test used Conclusion

Normality Help in knowing the shape of the distribution and helps to predict dependent variables scores

Shapiro-Wilk test Quartile-Quartile Plot (Q-Q plot)

-If P-value< 0.05, data is normally distributed For the fit to be done, the dependent variable scatter should be normally distributed Heteroscedasticity Checks whether the variance of

the dependent variable varies across the data (test the assumption of equal variance)

Glejser test If P-value< 0.05, presence of non-uniform variance Multicollinearity Check whether the correlations

among the independent variables are strong

Variance Inflation Factor (VIF)

If VIF for one of the variables is around or greater than 10, there is Multicollinearity

Tests of Independence (Autocorrelation)

check that the residuals of the models were not auto correlated (Checks for independence of error terms, which implies that

observations are independent

Durbin Watson (DW) test

Scores between 1.5 and 2.5 indicate independent observations

Operationalization of variables

Operationalization of variables table shows the relationship between strategy implementation independent variables and dependent variable on performance of Maritime cluster firms in Kenya. The dependent variable is the performance that will be measured against independent variables; strategic policies, organizational structure, Resource capability, Technology innovation, firms’ size and years of operation, and performance of maritime cluster firms in Kenya as shown in table 3.5

Data Presentation

This study’s results and findings will be presented by use of graphs, pie charts and tables. In this case, the use of frequency table will help in presenting data by assigned numerical value that shows percentages, valid percentages as well as cumulative percentages. The presentation percentages especially when indicated in graphic format are easily seen and understood (Cooper & Schindler, 2011).

Table 3.5 Operationalization of Variables

Research Hypothesis Measurement Analysis techniques Interpretation

Influence of strategy implementation on performance of

maritime cluster firms in Kenya.

All the independent Variables collectively and significantly

influence the Dependent Variable

Ha1 Strategic Policies have significant Influence on Performance of maritime

cluster firms in Kenya.

Ha2Organizational

structure has significant influence on

performance of maritime cluster firms in Kenya.

Ha3 There exists significant influence of resource capabilities on performance of

Maritime cluster firms in Kenya Quantitative Data Qualitative 5Point-Likert Scale Quantitative Data Qualitative 5Point-Likert Scale Quantitative Data Qualitative 5Point-Likert Scale Quantitative Data Qualitative 5Point-Likert Scale

Multiple Regression analysis Y = α + β1X1 + β2X2 + β3X3+ β4X4+

β5X5+ε)

Y=Performance of maritime cluster in Kenya

α= constant (intercept) X1-= is the composite index of

stakeholders’ engagement X2=is the composite index of

organizational structure X3=is, the composite index of

strategic

X4= is the composite index of

resource capability

X5= is the composite index of strategic direction

β1- β4- are the coefficients

ε -is the error term Compute coefficient of determination R2 _/k-1 R2 ₌ (1-R2_{) /n-k}

Multiple regression analysis Y= α+ β1X1 + ε

α =constant (intercept)

X1= is the composite index of

strategic policies ε = Error term Compute the P-value corresponding to β1

If P-value ≤ ₊ * reject H0

Multiple Regression analysis Y= α+ β2X2 + ε

α =constant (intercept)

X2= is the composite index of strategic leadership

ε = Error term Compute the P-value corresponding toΒ2

If P-value ≤ ₊ * reject H0

Multiple Régression analysis Y= α+ β3X3 + ε

α =constant (intercept)

X3= is the composite index of

organization structure ε = Error term

Compute the P-value

All the independent variables collectively influence the dependent variable Compute P Value given by SPSS. If P ≤ α/2, then there is a significant relationship between strategic policies and performance of maritime cluster firms in Kenya. Compute P Value given by SPSS. If P ≤ α/2, then there is a significant relationship between organization structure and performance of maritime cluster firms in Kenya Compute P Value given by SPSS. If P ≤ α/2, then there is a significant relationship between Resource capability and performance of maritime cluster firms in Kenya. Compute P Value

Ha4 There exists a relationship between technology innovation and performance of Maritime cluster firms in Kenya.

Ha5 There exists a relationship between strategic direction and performance of Maritime cluster firms in Kenya.

Ha6 Firm characteristics significant moderates the relationship between strategy implementation and performance of

maritime cluster firms in Kenya. Quantitative Data Qualitative 5Point-Likert Scale Quantitative Data Qualitative 5Point-Likert Scale Quantitative Data Qualitative 5 Point-Likert Scale corresponding to Β3 If P-value ≤ ₊ * , reject H0

Multiple Régression analysis Y= α+ β4X4+ε

α =constant (intercept)

X4= is the composite index of

resource capability ε = Error term Compute the P-value corresponding to Β4

If P-value ≤ *

+ , reject H0 Compute the P-value corresponding to Β5

If P-value ≤ *

+ reject H0 Multiple Régression analysis Y= α+ β5X5+ ε

α =constant (intercept)

X5= is the composite index of

firm’s size and years of operation ε = Error term

Compute the P-value corresponding to Β5

If P-value ≤ ₊ * reject H0

Compute the P-value corresponding to Β6

If P-value ≤ ₊ * reject H0

Multiple Régression analysis Y= α+ β6X6+ ε

α =constant (intercept)

X6= is the composite index of

firm’s characteristics ε = Error term Compute the P-value corresponding to Β5 If P-value ≤ ₊ * reject H0 given by SPSS. If P ≤ α/2, then there is a significant relationship between Technology innovation and performance of Maritime cluster firms in Kenya. Compute P Value given by SPSS. If P ≤ α/2, then there is a significant relationship between strategic direction and performance of Maritime cluster firms in Kenya. Compute P Value given by SPSS. If P ≤ α/2, then there is a significant relationship between firm characteristics, strategy implementation and performance of Maritime cluster firms in Kenya.

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